Editing Lemmatization (section)

==Description==
In many languages, words appear in several ''[[inflected]]'' forms. For example, in English, the verb 'to walk' may appear as 'walk', 'walked', 'walks' or 'walking'. The base form, 'walk', that one might look up in a dictionary, is called the ''lemma'' for the word. The association of the base form with a part of speech is often called a ''[[lexeme]]'' of the word.

Lemmatization is closely related to [[stemming]]. The difference is that a stemmer operates on a single word ''without'' knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech. However, stemmers are typically easier to implement and run faster. The reduced "accuracy" may not matter for some applications. In fact, when used within information retrieval systems, stemming improves query [[Precision and recall#Recall|recall accuracy]], or true positive rate, when compared to lemmatization. Nonetheless, stemming reduces [[Precision and recall#Recall|precision]], or the proportion of positively-labeled instances that are actually positive, for such systems.<ref name="Stanford Information Retrieval Book">{{cite web |url=http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html|title=Introduction to Information Retrieval|publisher=Cambridge University Press|first1=Christopher D.|last1=Manning|first2=Prabhakar|last2=Raghavan|first3=Hinrich|last3=Schütze}}</ref>

For instance:
#The word "better" has "good" as its lemma. This link is missed by stemming, as it requires a dictionary look-up.
#The word "walk" is the base form for the word "walking", and hence this is matched in both stemming and lemmatization.
#The word "meeting" can be either the base form of a noun or a form of a verb ("to meet") depending on the context; e.g., "in our last meeting" or "We are meeting again tomorrow". Unlike stemming, lemmatization attempts to select the correct lemma depending on the context.

Document indexing software like [[Apache Lucene|Lucene]]<ref name="Lucene Snowball" >{{cite web
|url=http://lucene.apache.org/core/
|title=Lucene Snowball
|publisher=Apache project
}}</ref> can store the base stemmed format of the word without the knowledge of meaning, but only considering word formation grammar rules. The stemmed word itself might not be a valid word: 'lazy', as seen in the example below, is stemmed by many stemmers to 'lazi'. This is because the purpose of stemming is not to produce the appropriate lemma&nbsp;– that is a more challenging task that requires knowledge of context. The main purpose of stemming is to map different forms of a word to a single form.<ref name="Porter Stemmer" >{{cite web
|url=http://tartarus.org/~martin/PorterStemmer/
|title=Porter Stemmer
|author=Martin Porter
}}</ref> As a rule-based algorithm, dependent only upon the spelling of a word, it sacrifices accuracy to ensure that, for example, when 'laziness' is stemmed to 'lazi', it has the same stem as 'lazy'.